AI: A Life Insurance Underwriter’s Best Friend

“The possibilities for a technology revolution in insurance are indeed endless with the rise of AI, robots, and robo-advisors. But what if this revolution could produce more heroes than casualties? What if rather than being replaced by these technologies, humans could be made better, stronger, and faster by them?”

The paper covers well-trodden ground with a brief and broad delineation of the tasks best suited for humans versus machines, but offers smart details for executives who are parsing the risks and benefits of AI for their firms. The real issue isn’t about what automation can replace, but what benefits it can bring to very human underwriters, analysts and adjustors.

“It’s about enhancement, not replacement,” say Woodley and Collamer. “Rather than replacing employees with robotic agents, insurance companies should invest their IT dollars to enhance the performance of agents and underwriters by providing them with better data and tools. Free the humans from the mundane administrative tasks easily handled by a machine.”

NTT Data’s research shows that senior underwriters spend over half their workday performing low-value activities, leaving less than half their days to do the more complex work they were hired to perform. “It has been estimated that underwriters spend 70% of their time performing low-value tasks, such as searching, aggregating, and selecting data, and only 30% of their time in risk selection,” it states. “By applying AI and machine learning to data aggregation and selection, you’ll enable underwriters to more quickly and easily identify the patterns that can help them reduce risk.”

AI can be leveraged to improve data quality, but significant issues around accessing, sharing and analyzing deeply siloed data will continue to be a thorny issue for many. “It is compounded by a lack of usability of the vast amount of data that exists beyond corporate systems in the carriers’ data ecosystem. To make greater use of all data, carriers need to: understand their data ecosystem (where the data lives); identify their data goals (what they need to know from the data); and put systems in place that can aggregate the meaningful data and serve it up in one place where it can be accessed, analyzed, and acted upon.”

The report offers a shortlist of questions to ask before investing in an AI initiative. Again, they are simple, but savvy:

Decide where AI makes the most sense within your organization. “Map the customer journey to identify opportunities for automation and self-service and understand where human interaction is required.”

Use AI to support your people. “By applying AI and machine learning to data aggregation and selection, you’ll enable underwriters to more quickly and easily identify the patterns that can help them reduce risk.”

Use AI to improve the quality of data and the way it is used. Insurers will continue to struggle with data access and quality issues as a result of decades of consolidation, and “(I)t is compounded by a lack of usability of the vast amount of data that exists beyond corporate systems in the carriers’ data ecosystem. To make greater use of all data, carriers need to: understand their data ecosystem (where the data lives); identify their data goals (what they need to know from the data); and put systems in place that can aggregate the meaningful data and serve it up in one place where it can be accessed, analyzed, and acted upon.”

As the life insurance sector builds confidence in the digital space, some firms are finally increasing automation in the underwriting processes. How they automate differs for each insurer, depending on their line of business, how old their system is, and their overall business strategies. Automating Life Insurance Underwriting – A Closer Look gets granular on the subject of underwriting and is a good, timely read.